scholarly journals Structural equation modeling for hypertension and type 2 diabetes based on multiple SNPs and multiple phenotypes

2019 ◽  
Author(s):  
Saebom Jeon ◽  
Ji-yeon Shin ◽  
Jaeyong Yee ◽  
Taesung Park ◽  
Mira Park
Keyword(s):  
Type 2 Diabetes ◽  
Structural Equation Modeling ◽  
Genetic Variants ◽  
Latent Variables ◽  
Structural Equation ◽  
Equation Modeling ◽  
Multiple Snps ◽  
Informative Snps ◽  
Intermediate Phenotypes

AbstractGenome-wide association studies (GWAS) have been successful in identifying genetic variants associated with complex diseases. However, association analyses between genotypes and phenotypes are not straightforward due to the complex relationships between genetic and environmental factors. Moreover, multiple correlated phenotypes further complicate such analyses.To resolve this complexity, we present an analysis using structural equation modeling (SEM). Unlike current methods that focus only on identifying direct associations between diseases and genetic variants such as single-nucleotide polymorphisms (SNPs), our method introduces the effects of intermediate phenotypes, which are related phenotypes distinct from the target, into the systematic genetic study of diseases. Moreover, we consider multiple diseases simultaneously in a single model. The procedure can be summarized in four steps: 1) selection of informative SNPs, 2) extraction of latent variables from the selected SNPs, 3) investigation of the relationships among intermediate phenotypes and diseases, and 4) construction of an SEM. As a result, a quantitative map can be drawn that simultaneously shows the relationship among multiple SNPs, phenotypes, and diseases.In this study, we considered two correlated diseases, hypertension and type 2 diabetes (T2D), which are known to have a substantial overlap in their disease mechanism and have significant public health implications. As intermediate phenotypes for these diseases, we considered three obesity-related phenotypes—subscapular skin fold thickness, body mass index, and waist circumference—as traits representing subcutaneous adiposity, overall adiposity, and abdominal adiposity, respectively. Using GWAS data collected from the Korea Association Resource (KARE) project, we applied the proposed SEM process. Among 327,872 SNPs, 24 informative SNPs were selected in the first step (p<1.0E-05). Ten latent variables were generated in step 2. After an exploratory analysis, we established a path diagram among phenotypes and diseases in step 3. Finally, in step 4, we produced a quantitative map with paths moving from specific SNPs to hypertension through intermediate phenotypes and T2D. The resulting model had high goodness-of fit measures (χ2= 536.52, NFI=0.997, CFI=0.998).

scholarly journals Structural equation modeling for hypertension and type 2 diabetes based on multiple SNPs and multiple phenotypes

PLoS ONE ◽  
2019 ◽  
Vol 14 (9) ◽  
pp. e0217189
Author(s):  
Saebom Jeon ◽  
Ji-yeon Shin ◽  
Jaeyong Yee ◽  
Taesung Park ◽  
Mira Park
Keyword(s):  
Type 2 Diabetes ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Multiple Snps ◽  
Multiple Phenotypes

scholarly journals Application of structural equation modeling in assessing the self-management activities of patients with type 2 diabetes

2020 ◽  
Vol 11 ◽  
pp. 215013272097420
Author(s):  
Rashid M. Ansari ◽  
Mark Harris ◽  
Hassan Hosseinzadeh ◽  
Nicholas Zwar
Keyword(s):  
Type 2 Diabetes ◽  
Structural Equation Modeling ◽  
Glycemic Control ◽  
Structural Equation ◽  
Self Care ◽  
The Self ◽  
Self Management ◽  
Equation Modeling ◽  
Chi Squared

Objective This study aimed at assessing the self-management activities of type 2 diabetes patients using Structural Equation Modeling (SEM) which measures and analyzes the correlations between observed and latent variables. This statistical modeling technique explored the linear causal relationships among the variables and accounted for the measurement errors. Methods A sample of 200 patients was recruited from the middle-aged population of rural areas of Pakistan to explore the self-management activities of type 2 diabetes patients using the validated version of the Urdu Summary of Diabetes Self-care Activities (U-SDSCA) instrument. The structural modeling equations of self-management of diabetes were developed and used to analyze the variation in glycemic control (HbA1c). Results The validated version of U-SDSCA instrument showed acceptable psychometric properties throughout a consecutive reliability and validity evaluation including: split-half reliability coefficient 0.90, test-retest reliability (r = 0.918, P  ≤ .001), intra-class coefficient (0.912) and Cronbach’s alpha (0.79). The results of the analysis were statistically significant (α = 0.05, P-value < .001), and showed that the model was very well fitted with the data, satisfying all the parameters of the model related to confirmatory factor analysis with chi-squared = 48.9, CFI = 0.94, TLI = 0.95, RMSEA = 0.065, SPMR = 0.068. The model was further improved once the items related to special diet were removed from the analysis, chi-squared value (30.895), model fit indices (CFI = 0.98, TLI = 0.989, RMSEA = 0.045, SPMR = 0.048). A negative correlation was observed between diabetes self-management and the variable HbA1c (r = –0.47; P < .001). Conclusions The Urdu Summary of Diabetes Self-Care Activities (U-SDSCA) instrument was used for the patients of type 2 diabetes to assess their diabetes self-management activities. The structural equation models of self-management showed a very good fit to the data and provided excellent results which may be used in future for clinical assessments of patients with suboptimal diabetes outcomes or research on factors affecting the associations between self-management activities and glycemic control

Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

2019 ◽  
Vol 50 (1) ◽  
pp. 24-37
Author(s):  
Ben Porter ◽  
Camilla S. Øverup ◽  
Julie A. Brunson ◽  
Paras D. Mehta
Keyword(s):  
Structural Equation Modeling ◽  
Social Relations ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Complete Understanding ◽  
Social Relations Model ◽  
Flexible Framework ◽  
Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

Building a Model of Family Caregiving for Children with Emotional Disorders

1997 ◽  
Vol 5 (3) ◽  
pp. 138-148 ◽  
Author(s):  
Thomas P. Mcdonald ◽  
Thomas K. Gregoire ◽  
John Poertner ◽  
Theresa J. Early
Keyword(s):  
Structural Equation Modeling ◽  
Emotional Disorders ◽  
Problem Behaviors ◽  
Family Caregiving ◽  
Latent Variables ◽  
Structural Equation ◽  
Empirical Literature ◽  
Equation Modeling ◽  
Special Challenge ◽  
And Coping

In this article we describe the results of an ongoing effort to better understand the caregiving process in families of children with severe emotional problems. We make two assumptions. First, we assume that these families are essentially like other families but are faced with a special challenge in raising and caring for their special children while at the same time performing the multiple tasks and demands faced by all families. Second, we assume that public policy and programs must be supportive of the care of these children in their own homes and communities whenever possible. The purpose of this article is to present a model of family caregiving that draws broadly from available theory and empirical literature in multiple fields and to subject this model to empirical testing. We use structural equation modeling with latent variables to estimate an empirical model based on the theoretical model. Results of the model testing point to the importance of the child's external problem behaviors and the family's socioeconomic status and coping strategies as determinants of caregiver stress. Other findings highlight difficulties in measuring and modeling the complex mediating process, which includes formal and informal supports, perceptions, and coping behaviors. The use of structural equation modeling can benefit our efforts to support families by making explicit our theories about the important dimensions of this process and the relationship between these dimensions, which can then be subjected to measurement and validation.

scholarly journals Structural Equation Modeling for the Effect of Main Factors on Abortion Issue

2019 ◽  
Vol 7 (1) ◽  
pp. 1-13
Author(s):  
Aras Jalal Mhamad ◽  
Renas Abubaker Ahmed
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Close Contact ◽  
Maternity Hospital ◽  
Equation Modeling ◽  
Research Model ◽  
Statistical Tools ◽  
Main Factors

       Based on medical exchange and medical information processing theories with statistical tools, our study proposes and tests a research model that investigates main factors behind abortion issue. Data were collected from the survey of Maternity hospital in Sulaimani, Kurdistan-Iraq. Structural Equation Modelling (SEM) is a powerful technique as it estimates the causal relationship between more than one dependent variable and many independent variables, which is ability to incorporate quantitative and qualitative data, and it shows how all latent variables are related to each other. The dependent latent variable in SEM which have one-way arrows pointing to them is called endogenous variable while others are exogenous variables. The structural equation modeling results reveal is underlying mechanism through which statistical tools, as relationship between factors; previous disease information, food and drug information, patient address, mother’s information, abortion information, which are caused abortion problem. Simply stated, the empirical data support the study hypothesis and the research model we have proposed is viable. The data of the study were obtained from a survey of Maternity hospital in Sulaimani, Kurdistan-Iraq, which is in close contact with patients for long periods, and it is number one area for pregnant women to obtain information about the abortion issue. The results shows arrangement about factors effectiveness as mentioned at section five of the study. This gives the conclusion that abortion problem must be more concern than the other pregnancy problem.

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